System for determining confidence in respiratory rate measurements

ABSTRACT

This disclosure describes, among other features, systems and methods for using multiple physiological parameter inputs to determine multiparameter confidence in respiratory rate measurements. For example, a patient monitoring system can programmatically determine multiparameter confidence in respiratory rate measurements obtained from an acoustic sensor based at least partly on inputs obtained from other non-acoustic sensors or monitors. The patient monitoring system can output a multiparameter confidence indication reflective of the programmatically-determined multiparameter confidence. The multiparameter confidence indication can assist a clinician in determining whether or how to treat a patient based on the patient&#39;s respiratory rate.

RELATED APPLICATIONS

This application claims priority from U.S. Provisional PatentApplication No. 61/252,086 filed Oct. 15, 2009, entitled “Pulse OximetrySystem for Determining Confidence in Respiratory Rate Measurements,”from U.S. Provisional Patent Application No. 61/261,199, filed Nov. 13,2009, entitled “Pulse Oximetry System with Adjustable Alarm Delay,” andfrom U.S. Provisional Patent Application No. 61/366,866, filed Jul. 22,2010, entitled “Pulse Oximetry System for Determining Confidence inRespiratory Rate Measurements,” the disclosures of which are herebyincorporated by reference in their entirety.

BACKGROUND

Hospitals, nursing homes, and other patient care facilities typicallyinclude patient monitoring devices at one or more bedsides in thefacility. Patient monitoring devices generally include sensors,processing equipment, and displays for obtaining and analyzing apatient's physiological parameters. Physiological parameters include,for example, blood pressure, respiratory rate, oxygen saturation (SpO₂)level, other blood constitutions and combinations of constitutions, andpulse, among others. Clinicians, including doctors, nurses, and certainother caregiver personnel use the physiological parameters obtained fromthe patient to diagnose illnesses and to prescribe treatments.Clinicians can also use the physiological parameters to monitor apatient during various clinical situations to determine whether toincrease the level of care given to the patient. Various patientmonitoring devices are commercially available from Masimo Corporation(“Masimo”) of Irvine, Calif.

During and after surgery and in other care situations, respiratory rateis a frequently monitored physiological parameter of a patient.Respiratory rate can be indicated as the number of breaths a persontakes within a certain amount of time, such as breaths per minute. Forexample, a clinician (such as a nurse, doctor, or the like) can userespiratory rate measurements to determine whether a patient isexperiencing respiratory distress and/or dysfunction.

SUMMARY OF DISCLOSURE

A system for determining multiparameter confidence in a respiratory ratemeasurement from a medical patient, the system comprising: an opticalsensor comprising: a light emitter configured to impinge light on bodytissue of a living patient, the body tissue comprising pulsating blood,and a detector responsive to the light after attenuation by the bodytissue, wherein the detector is configured to generate aphotoplethysmographic signal indicative of a physiologicalcharacteristic of the living patient; an ECG sensor configured to obtainan electrical signal from the living patient; an acoustic sensor, theacoustic sensor configured to obtain an acoustic respiratory signal fromthe living patient; and a processor configured to: derive a firstrespiratory rate measurement from the acoustic respiratory signal,derive a second respiratory rate measurement from one or both of thephotoplethysmographic signal and the electrical signal, and use thesecond respiratory rate measurement to calculate a confidence in thefirst respiratory rate measurement.

A system for determining confidence in a respiratory rate measurementfrom a medical patient, the system comprising: a first physiologicalsensor configured to obtain a physiological signal from a patient, thefirst physiological sensor comprising at least one of the following: anECG sensor, a bioimpedance sensor, and a capnography sensor; an acousticsensor configured to obtain an acoustic respiratory signal from theliving patient; and a processor configured to: obtain a firstrespiratory rate measurement from the physiological signal, obtain asecond respiratory rate measurement from the acoustic respiratorysignal, and calculate a confidence in the first respiratory ratemeasurement responsive to the first and second respiratory ratemeasurements.

A method of analyzing respiratory rate monitoring parameters todetermine confidence in a measured respiratory rate, the methodcomprising: obtaining a first respiratory measurement from a firstphysiological device, the first physiological device comprising anacoustic sensor; obtaining a second respiratory measurement from asecond physiological device; determining a third respiratory ratemeasurement based at least in part on the first and second respiratoryrate measurements; and outputting the third respiratory ratemeasurement.

For purposes of summarizing the disclosure, certain aspects, advantagesand novel features of the inventions have been described herein. It isto be understood that not necessarily all such advantages can beachieved in accordance with any particular embodiment of the inventionsdisclosed herein. Thus, the inventions disclosed herein can be embodiedor carried out in a manner that achieves or optimizes one advantage orgroup of advantages as taught herein without necessarily achieving otheradvantages as can be taught or suggested herein.

BRIEF DESCRIPTION OF THE DRAWINGS

Throughout the drawings, reference numbers can be re-used to indicatecorrespondence between referenced elements. The drawings are provided toillustrate embodiments of the inventions described herein and not tolimit the scope thereof.

FIGS. 1A-B are block diagrams illustrating physiological monitoringsystems in accordance with embodiments of the disclosure;

FIG. 1C is a top perspective view illustrating portions of a sensorassembly in accordance with an embodiment of the disclosure;

FIG. 2 illustrates a block diagram of an embodiment of a multiparameterpatient monitoring system that includes an acoustic respiratorymonitoring (ARM) sensor and an optical sensor.

FIG. 3A illustrates an embodiment of an envelope of aphotoplethysmograph waveform.

FIG. 3B schematically illustrates an example calculation of pulse wavetransit time from two physiological signal inputs.

FIG. 3C illustrates example power spectrum plots of pulse wave transittime variability and heart rate variability for determining respiratoryrate measurements.

FIG. 4 illustrates an embodiment of the multiparameter patientmonitoring system of FIG. 2 coupled to a patient.

FIG. 5 illustrates a block diagram of an embodiment of a multiparameterpatient monitoring system.

FIGS. 6A through 6C illustrate block diagrams of embodiments ofrespiratory rate measurement calculation systems.

FIG. 7 illustrates an example multiparameter physiological monitor.

FIGS. 8A through 8D illustrate example multiparameter physiologicalmonitor displays.

FIG. 9 illustrates an embodiment of a patient monitoring process inwhich a user can specify a delay time for an alarm to be triggered.

FIG. 10 illustrates an embodiment of a multiparameter patient monitoringprocess that allows for dynamic modification of an alarm delay.

FIGS. 11 through 17 illustrate embodiments of parameter confidencedisplays.

DETAILED DESCRIPTION

Acoustic sensors, including piezoelectric acoustic sensors, can be usedto measure breath sounds and other biological sounds of a patient.Breath sounds obtained from an acoustic sensor placed on the neck,chest, and/or other suitable location can be processed by a patientmonitor to derive one or more physiological parameters of a patient,including respiratory rate. Respiratory rate can also be determined fromother physiological signals (for example, an ECG signal, aplethysmographic signal, a bioimpedance signal, and/or the like)obtained using other sensors and/or instruments.

Respiratory rate measurements derived from a single sensor or sensortype can be less accurate at times due to noise, sensor limitations,body movement, and/or other reasons. Accordingly, improvedmultiparameter respiratory rate measurements can be obtained by jointlyprocessing multiple physiological signals from multiple sensors and/orsensor types. Alternately, or in addition, respiratory rate measurementsderived from a physiological signal obtained from one type of sensor canbe used to continuously or periodically refine or assess confidence inthe respiratory rate measurements derived from a physiological signalobtained from another type of sensor. For example, in certainembodiments, respiratory rate measurements derived from aplethysmographic signal obtained by an optical sensor can be used toimprove or determine confidence in the respiratory rate derived from anacoustic signal obtained by an acoustic sensor.

This disclosure describes, among other features, systems and methods forusing multiple physiological signals to improve respiratory or otherphysiological parameter measurements reflective of a patient's conditionand/or to determine confidence in these physiological parametermeasurements. In certain embodiments, a patient monitoring systemcomprises one or more physiological sensors applied to a living patientand a processor to monitor the physiological signals received fromphysiological sensors. The physiological sensors can include, forexample, acoustic sensors for acquiring breath and/or heart sounds,electrodes for acquiring ECG and/or bioimpedance signals, andnoninvasive optical sensors to perform pulse oximetry and relatednoninvasive analysis of blood constituents.

In particular, in certain embodiments, a patient monitoring system canprogrammatically determine multiparameter confidence in respiratory ratemeasurements obtained from an acoustic sensor based at least partly oninputs obtained from other non-acoustic sensors or monitors. The patientmonitoring system can output a multiparameter confidence indicationreflective of the programmatically-determined multiparameter confidence.The multiparameter confidence indication can assist a clinician indetermining whether or how to treat a patient based on the patient'srespiratory rate.

In certain embodiments, the patient monitoring system can determine themultiparameter confidence at least in part by receiving signals frommultiple physiological parameter monitoring devices that are reflectiveof respiratory rate. For example, a multiparameter patient monitoringunit can receive a signal reflective of a respiratory rate from both anacoustic sensor and an optical sensor. Respiratory rate measurements canbe extracted and/or derived from each of the physiological parametersignals. The respiratory rate measurement from the acoustic sensor canbe compared with the respiratory rate measurement derived from theoptical sensor signal. Based at least partly on this comparison, adetermination of multiparameter confidence in the acoustically-derivedrespiratory rate can be made. A visual or audible indicatorcorresponding to this multiparameter confidence determination, includingpossibly an alarm, can be output for presentation to a clinician.

Additionally, in certain embodiments, the respiratory rate measurementoutput to the clinician can be generated based at least partly on acombination of the multiple respiratory rate measurements. For example,a respiratory rate measurement derived from an optical sensor signal canbe combined with a respiratory rate measurement derived from an acousticsensor signal to produce an overall respiratory rate. The overallrespiratory rate measurement can be output to the patient monitordisplay and/or can be output over a network to another device.

Moreover, in certain embodiments, the patient monitoring systems andmethods disclosed herein can assess multiparameter confidence and/ordetermine respiratory rate based at least partly on signals receivedfrom other physiological parameter monitoring devices. For example,various measurements obtained from a capnograph, an electrocardiograph(ECG), a bioimpedance device, or from other monitoring devices orsensors can be used to assess multiparameter confidence in acousticrespiratory rate measurements and/or to determine an overall respiratoryrate output.

For purposes of illustration, this disclosure is described primarily inthe context of respiratory rate. However, the features described hereincan be applied to other respiratory parameters, including, for example,inspiratory time, expiratory time, inspiratory to expiratory ratio,inspiratory flow, expiratory flow, tidal volume, minute volume, apneaduration, breath sounds (including, e.g., rales, rhonchi, or stridor),changes in breath sounds, and the like. Moreover, the features describedherein can also be applied to other physiological parameters and/orvital signs. For example, outputs from multiple monitoring devices orsensors (e.g., an optical sensor and an ECG monitor) can be used toassess multiparameter confidence in heart rate measurements, among otherparameters.

Referring to the drawings, FIGS. 1A through 1C illustrate examplepatient monitoring systems, sensors, and cables that can be used toderive a respiratory rate measurement from a patient. FIGS. 2 through 8illustrate multiparameter respiratory rate embodiments. The embodimentsof FIGS. 2 through 8 can be implemented at least in part using thesystems and sensors described in FIGS. 1A through 1C.

Turning to FIG. 1A, an embodiment of a physiological monitoring system10 is shown. In the physiological monitoring system 10, a medicalpatient 12 is monitored using one or more sensor assemblies 13, each ofwhich transmits a signal over a cable 15 or other communication link ormedium to a physiological monitor 17. The physiological monitor 17includes a processor 19 and, optionally, a display 11. The one or moresensors 13 include sensing elements such as, for example, acousticpiezoelectric devices, electrical ECG leads, optical sensors, or thelike. The sensors 13 can generate respective signals by measuring aphysiological parameter of the patient 12. The signals are thenprocessed by one or more processors 19. The one or more processors 19then communicate the processed signal to the display 11. In anembodiment, the display 11 is incorporated in the physiological monitor17. In another embodiment, the display 11 is separate from thephysiological monitor 17. In one embodiment, the monitoring system 10 isa portable monitoring system. In another embodiment, the monitoringsystem 10 is a pod, without a display, that is adapted to providephysiological parameter data to a display.

For clarity, a single block is used to illustrate the one or moresensors 13 shown in FIG. 1A. It should be understood that the sensor 13shown is intended to represent one or more sensors. In an embodiment,the one or more sensors 13 include a single sensor of one of the typesdescribed below. In another embodiment, the one or more sensors 13include one or more acoustic sensors. In still another embodiment, theone or more sensors 13 include one or more acoustic sensors and one ormore ECG sensors, optical sensors, bioimpedance sensors, capnographysensors, and the like. In each of the foregoing embodiments, additionalsensors of different types are also optionally included. Othercombinations of numbers and types of sensors are also suitable for usewith the physiological monitoring system 10.

In some embodiments of the system shown in FIG. 1A, all of the hardwareused to receive and process signals from the sensors are housed withinthe same housing. In other embodiments, some of the hardware used toreceive and process signals is housed within a separate housing. Inaddition, the physiological monitor 17 of certain embodiments includeshardware, software, or both hardware and software, whether in onehousing or multiple housings, used to receive and process the signalstransmitted by the sensors 13.

As shown in FIG. 1B, the acoustic sensor assembly 13 can include a cable25. The cable 25 can include three conductors within an electricalshielding. One conductor 26 can provide power to a physiological sensor13, one conductor 28 can provide a ground signal from the physiologicalmonitor 17, and one conductor 28 can transmit signals from the sensor 13to the physiological monitor 17. For multiple sensors 13, one orpossibly more cables 13 can be provided.

In some embodiments, the ground signal is an earth ground, but in otherembodiments, the ground signal is a patient ground, sometimes referredto as a patient reference, a patient reference signal, a return, or apatient return. In some embodiments, the cable 25 carries two conductorswithin an electrical shielding layer, and the shielding layer acts asthe ground conductor. Electrical interfaces 23 in the cable 25 canenable the cable to electrically connect to electrical interfaces 21 ina connector 20 of the physiological monitor 17. In another embodiment,the sensor assembly 13 and the physiological monitor 17 communicatewirelessly.

FIG. 1C illustrates an embodiment of a sensor system 100 including asensor assembly 101 and a monitor cable 111 suitable for use with any ofthe physiological monitors shown in FIGS. 1A and 1B. The sensor assembly101 includes a sensor 115, a cable assembly 117, and a connector 105.The sensor 115, in one embodiment, includes a sensor subassembly 102 andan attachment subassembly 104. The cable assembly 117 of one embodimentincludes a sensor 107 and a patient anchor 103. A sensor connectorsubassembly 105 is connected to the sensor cable 107.

The sensor connector subassembly 105 can be removably attached to aninstrument cable 111 via an instrument cable connector 109. Theinstrument cable 111 can be attached to a cable hub 120, which includesa port 121 for receiving a connector 112 of the instrument cable 111 anda second port 123 for receiving another cable. In certain embodiments,the second port 123 can receive a cable connected to an optical sensoror other sensor. In addition, the cable hub 120 could include additionalports in other embodiments for receiving additional cables. The hubincludes a cable 122 which terminates in a connector 124 adapted toconnect to a physiological monitor (not shown).

The sensor connector subassembly 105 and connector 109 can be configuredto allow the sensor connector 105 to be straightforwardly andefficiently joined with and detached from the connector 109. Embodimentsof connectors having connection mechanisms that can be used for theconnectors 105, 109 are described in U.S. patent application Ser. No.12/248,856 (hereinafter referred to as “the '856 Application”), filed onOct. 9, 2008, which is incorporated in its entirety by reference herein.For example, the sensor connector 105 could include a mating feature(not shown) which mates with a corresponding feature (not shown) on theconnector 109. The mating feature can include a protrusion which engagesin a snap fit with a recess on the connector 109. In certainembodiments, the sensor connector 105 can be detached via one handoperation, for example. Examples of connection mechanisms can be foundspecifically in paragraphs [0042], [0050], [0051], [0061]-[0068] and[0079], and with respect to FIGS. 8A-F, 13A-E, 19A-F, 23A-D and 24A-C ofthe '856 Application, for example.

The sensor connector subassembly 105 and connector 109 can reduce theamount of unshielded area in and generally provide enhanced shielding ofthe electrical connection between the sensor and monitor in certainembodiments. Examples of such shielding mechanisms are disclosed in the'856 Application in paragraphs [0043]-[0053], [0060] and with respect toFIGS. 9A-C, 11A-E, 13A-E, 14A-B, 15A-C, and 16A-E, for example.

In an embodiment, the acoustic sensor assembly 101 includes a sensingelement, such as, for example, a piezoelectric device or other acousticsensing device. The sensing element can generate a voltage that isresponsive to vibrations generated by the patient, and the sensor caninclude circuitry to transmit the voltage generated by the sensingelement to a processor for processing. In an embodiment, the acousticsensor assembly 101 includes circuitry for detecting and transmittinginformation related to biological sounds to a physiological monitor.These biological sounds can include heart, breathing, and/or digestivesystem sounds, in addition to many other physiological phenomena. Theacoustic sensor 115 in certain embodiments is a biological sound sensor,such as the sensors described herein. In some embodiments, thebiological sound sensor is one of the sensors such as those described inthe '883 Application. In other embodiments, the acoustic sensor 115 is abiological sound sensor such as those described in U.S. Pat. No.6,661,161, which is incorporated by reference herein in its entirety.Other embodiments include other suitable acoustic sensors.

The attachment sub-assembly 104 includes first and second elongateportions 106, 108. The first and second elongate portions 106, 108 caninclude patient adhesive (e.g., in some embodiments, tape, glue, asuction device, etc.). The adhesive on the elongate portions 106, 108can be used to secure the sensor subassembly 102 to a patient's skin.One or more elongate members 110 included in the first and/or secondelongate portions 106, 108 can beneficially bias the sensor subassembly102 in tension against the patient's skin and reduce stress on theconnection between the patient adhesive and the skin. A removablebacking can be provided with the patient adhesive to protect theadhesive surface prior to affixing to a patient's skin.

The sensor cable 107 can be electrically coupled to the sensorsubassembly 102 via a printed circuit board (“PCB”) (not shown) in thesensor subassembly 102. Through this contact, electrical signals arecommunicated from the multi-parameter sensor subassembly to thephysiological monitor through the sensor cable 107 and the cable 111.

In various embodiments, not all of the components illustrated in FIG. 1Care included in the sensor system 100. For example, in variousembodiments, one or more of the patient anchor 103 and the attachmentsubassembly 104 are not included. In one embodiment, for example, abandage or tape is used instead of the attachment subassembly 104 toattach the sensor subassembly 102 to the measurement site. Moreover,such bandages or tapes can be a variety of different shapes includinggenerally elongate, circular and oval, for example. In addition, thecable hub 120 need not be included in certain embodiments. For example,multiple cables from different sensors could connect to a monitordirectly without using the cable hub 120.

Additional information relating to acoustic sensors compatible withembodiments described herein, including other embodiments of interfaceswith the physiological monitor, are included in U.S. patent applicationSer. No. 12/044,883, filed Mar. 7, 2008, entitled “Systems and Methodsfor Determining a Physiological Condition Using an Acoustic Monitor,”(hereinafter referred to as “the '883 Application”), the disclosure ofwhich is hereby incorporated by reference in its entirety. An example ofan acoustic sensor that can be used with the embodiments describedherein is disclosed in U.S. Patent Application No. 61/252,076, filedOct. 15, 2009, titled “Acoustic Sensor Assembly,” the disclosure ofwhich is hereby incorporated by reference in its entirety.

FIG. 2 illustrates an embodiment of a multiparameter patient monitoringsystem 200, which can implement any of the features described above. Themultiparameter patient monitoring system 200 includes a multiparameterpatient monitor 205 that receives signals from multiple physiologicalparameter measurement devices. The multiparameter patient monitor 205can use the multiple received signals to determine a confidence valuefor respiratory rate measurements derived from the signals. Theconfidence value can advantageously reflect a degree to which therespiratory rate measurements derived from the different signalscorrespond. In addition, in some embodiments, the multiparameter patientmonitor 205 can generate one or more respiratory rate outputs based atleast partly on the multiple received signals.

The patient monitor 205 can include any of the features of thephysiological monitor 17 described above. The patient monitor 205 caninclude one or more processors, a display, memory, one or moreinput/output (I/O) devices (such as input control buttons, speakers,etc), a wireless transceiver, a power supply, and/or processing andfiltration circuitry. In certain embodiments, the patient monitor 205can communicate with external devices, such as processing devices,output devices, mass storage devices, and the like. The patient monitor205 can communicate with the external devices via a wired and/orwireless connection. The external devices can include a centralmonitoring station (such as a nurses' monitoring station), a server, alaptop computer, a cell phone, a smart phone, a personal digitalassistant, a kiosk, other patient monitors, or other clinician devices.The patient monitor 205 can send physiological data to the externaldevices.

In the depicted embodiment, the patient monitor 205 is in communicationwith an acoustic sensor 210 and an optical sensor 210. The acousticsensor 210 can be a piezoelectric sensor or the like that obtainsphysiological information reflective of one or more respiratoryparameters of a patient, including respiratory rate, expiratory flow,tidal volume, minute volume, apnea duration, breath sounds, riles,rhonchi, stridor, and changes in breath sounds, such as decreased volumeor change in airflow. In addition, in some cases the acoustic sensor 210can measure other physiological sounds, such as heart rate (e.g., tohelp with probe-off detection). In certain embodiments, the acousticsensor 210 can include any of the features described in U.S. PatentApplication No. 61/252,076, filed Oct. 15, 2009, titled “Acoustic SensorAssembly,” the disclosure of which is hereby incorporated by referencein its entirety.

The optical sensor 215 can include a noninvasive optical sensor thatobtains physiological information reflective of one or more bloodparameters of the patient. These parameters can include one or more ofthe following: a photoplethysmograph, oxygen saturation (SpO₂), HbCO,HBMet, FaO₂, fractional oxygen, total hemoglobin (Hbt), other hemoglobinspecies, carbon monoxide, carbon dioxide, pulse rate, perfusion index,pleth variability index, and optionally others, including concentrationsor actual analyte values of the same. The optical sensor 215 can includeone or more emitters capable of irradiating a tissue site (such as afinger) with one or more wavelengths of light, such as red and/orinfrared (IR) wavelengths. In one embodiment, the optical sensor 215 isa pulse oximetry sensor. While many optical sensors emit twowavelengths, certain of the features described herein can be implementedby a photoplethysmograph sensor that emits a single wavelength. Further,the optical sensor 215 need not emit red or infrared wavelengths incertain embodiments but can also emit other wavelengths. The opticalsensor 215 can also include one or more detectors capable of detectingthe light after attenuation by pulsatile blood and tissue at themeasurement site. The one or more detectors can generate a signalresponsive to the attenuated light, which can be provided to the patientmonitor 205.

The patient monitor 205 can receive signals indicative of one or morephysiological parameters from the acoustic sensor 210 and from theoptical sensor 215. The patient monitor 205 can extract and/or deriverespiratory rate measurements from signals provided by both the acousticsensor 210 and the optical sensor 215. The patient monitor 205 can alsooutput one or more respiratory rate measurements for display based atleast in part on the received signals. Example techniques for derivingrespiratory rate from the optical sensor measurements are describedbelow with respect to FIG. 3.

In certain embodiments, the patient monitor 205 can use pulse oximetryrespiratory rate measurements to determine a multiparameter confidencein the acoustic respiratory rate measurements. The multiparameterconfidence can be a value that reflects a degree of correspondencebetween the respiratory rate measurements obtained from the two sensors210, 215. A close correspondence (e.g., small difference) between thetwo respiratory rate measurements can cause the patient monitor 205 toassign a higher multiparameter confidence to the acoustic respiratoryrate measurement. Conversely, a larger difference between the twomeasurements can result in a lower multiparameter confidence. In certainembodiments, the patient monitor 205 can instead or also use thedifference in respiratory rate values to assign a multiparameterconfidence to the pulse-oximetry-derived respiratory rate measurement.

More generally, any comparative metric can be used to determine themultiparameter confidence. The comparative metric can be a differencebetween the measurements of the two sensors 210, 215 but need not be.Instead, in some embodiments, the comparative metric can be a ratiobetween the measurements from the sensors 210, 215, a percentage derivedfrom such a ratio, or the like. Such a ratio or percentage might be moremeaningful than an absolute difference in some situations. Similarly,the comparative metric can be a normalization of the measurements fromthe two sensors 210, 215, such as the following quotient: (the acousticrespiratory rate—the oximeter respiratory rate)/(the acousticrespiratory rate) or the like. Other comparative metrics can also beused.

Additionally, the patient monitor 205 can use pulse oximetry respiratoryrate measurements to refine or adjust the acoustic respiratory ratemeasurements in some implementations. For example, the respiratory ratemeasurements derived from the two sensors 210, 215 can be combined toform an overall respiratory measurement. The patient monitor 205 canaverage the two measurements, for example. The combined respiratory ratemeasurement can be more accurate than a respiratory rate measurementfrom either sensor 210, 215 alone.

The patient monitor 205 can output the respiratory rate measurementderived from either or both of the acoustic and optical sensors 210,215. In addition, the patient monitor 205 can output a multiparameterconfidence indicator that reflects the calculated multiparameterconfidence. Examples of multiparameter confidence indicators aredescribed in greater detail below.

In certain embodiments, a signal received from the optical sensor 215can be analyzed to determine a respiratory rate measurement. As anillustration of such a signal, FIG. 3A depicts an examplephotoplethysmograph (pleth) waveform 300 derived from an optical sensor.The pleth waveform 300 can be derived from the received signal by thepatient monitor 205. The pleth waveform 300 is plotted on an intensityaxis 301 versus a time axis 302. The pleth waveform 300 has multiplepulses 312, each with a peak 314 and a valley 316 and extending over atime period 318. A curve extending along the peaks 314 of the plethwaveform 300 represents an envelope 322 of the pleth waveform 300.

In certain embodiments, a respiratory rate measurement can be determinedfrom an analysis of the pleth waveform 300. A respiratory ratemeasurement can be determined from the pleth waveform 300 in the timedomain and/or in the frequency domain. In certain embodiments, arespiratory rate measurement can be determined from the modulation inthe amplitude of the pleth waveform 300. For example, the time-varyingfrequency of the envelope 322 can correspond to the respiratory rate ofthe patient. The frequency of the pleth envelope 322 can be determinedfrom the inverse of the period 324 of the envelope 322. The envelope 322of the pleth waveform 300 can be detected by an envelope detector. Theenvelope can be identified using an analog envelope detector such as adiode-based envelope detector or a digital detector employing suchtechniques as a Hilbert transform, squaring and low-pass filtering, orthe like.

The respiratory rate can also be determined from a frequency analysis ofthe pleth waveform 300. A frequency spectrum of the pleth waveform 300can be generated, for example, by performing a Fast Fourier Transform(FFT) or other mathematical transform of the pleth waveform 300. Therespiratory rate can be identified by a peak in the spectrum (e.g.,which corresponds to the frequency of the pleth envelope 322). Incertain embodiments, the peak can be identified by identifying thehighest peak in a range of typical respiratory rates of a human patient.This range can differ for different patients based on factors such asage, gender, comorbidity, and the like. A respiratory rate value can bederived from the frequency of the selected peak. Additional methods ofdetermining respiratory rate from the pleth waveform 300 and/or anoptical signal are also possible.

In certain embodiments, instead of or in addition to analyzing the plethwaveform 300 to obtain respiratory rate, the patient monitor 205 canobtain respiratory rate from variability detected in oxygen saturationmeasurements obtained from the optical sensor 215. Variations in theoxygen saturation can track or approximately track the patient'srespiratory cycle (e.g., a cycle of recruitment and collapse ofalveoli), as is described in greater detail in U.S. Application No.61/222,087, filed Jun. 30, 2009, titled “Pulse Oximetry System forAdjusting Medical Ventilation,” the disclosure of which is herebyincorporated by reference in its entirety. The magnitude of thetime-domain variations in the oxygen saturation can reflect the degreeof recruitment and collapse of alveoli in the respiratory cycle. In thefrequency domain, a peak in a magnitude response of the SpO₂ variabilitywithin an expected respiratory rate range can be used to determine arespiratory rate measurement.

In certain embodiments, the patient monitor 205 can obtain a respiratoryrate measurement from variability detected in a patient's heart rate.The heart rate can be derived from an ECG signal, a bioimpedance signal,an acoustic signal, a plethysmograph signal, and/or combinations of thesame.

In one embodiment, an instantaneous heart rate can be derived bydetermining the interval between successive R waves of the ECG signaland then converting the interval to beats per minute (bpm). For example,the heart rate can be calculated as 60 divided by the R-R interval inseconds. In another embodiment, the instantaneous heart rate can bederived from successive peaks in the plethysmograph signal. For example,the instantaneous heart rate can be calculated as 60 divided by theinterval in seconds between the two successive peaks.

Other techniques can be used to derive the heart rate. For instance, theheart rate can be determined by analyzing any successive landmark of anECG or plethysmograph signal. Further, to improve noise immunity, thepatient monitor 205 can use a more robust technique to measure theinterval, such as autocorrelation of the ECG or plethysmograph waveformfrom one beat to the next. More generally, any technique for reliablymeasuring the period from one beat to the next can be used.

The instantaneous heart rate can be plotted over time to illustratevariability in the patient's heart rate. In certain embodiments, thevariability in the patient's heart rate is reflective of the patient'srespiratory rate. For example, analysis of the variability in theinstantaneous heart rate in the frequency domain (for example, by takingthe Fourier transform of the instantaneous heart rate signal in the timedomain) can provide an indication of respiratory rate that can be usedto assess confidence in a respiratory rate measurement derived from anacoustic sensor or another type of sensor.

In certain embodiments, the patient monitor 205 can also obtain arespiratory rate measurement by measuring arterial pulse wavepropagation time from the heart to an extremity. This propagation timeis typically used by blood pressure monitoring systems and can beestimated by detecting a time difference between points on an ECGwaveform and a photoplethysmograph waveform. This estimated propagationtime is sometimes referred to as pulse wave transit time (PWTT) or timedifference of arrival (TDOA). Currently available blood pressuremonitoring systems trigger an automatic occlusive cuff to take a bloodpressure measurement based on detected changes in PWTT.

Variability in the PWTT can be modulated by respiration. Thus, incertain embodiments, the patient monitor 205 can calculate PWTT anddetermine the variability in PWTT measurements over time. The patientmonitor 205 can derive respiratory rate values from the calculatedvariability. The patient monitor 205 can use these values to improve theaccuracy of or calculate confidence in acoustically-derived respiratoryvalues.

As illustrated in FIG. 3B, in one embodiment, PWTT is determined as atime difference between a peak of an R-wave 335 of a QRS complex of anECG signal 330 to the foot point 345 of a plethysmograph signal 340. TheR-wave 335 represents the first upward, or positive, deflection of theQRS complex and corresponds to the time of ventricular depolarization.The foot point 345 of the plethysmograph signal 340 can correspond tothe time of earliest onset of arrival of the pulse at a location awayfrom the heart (e.g., at a patient's finger). More generally, PWTT canbe taken as a time interval from any feature of the ECG waveform to anyfeature of the pleth waveform. For example, PWTT can be taken as theinterval between the Q or S points of the ECG waveform and a point suchas the midpoint of the pleth waveform.

The PWTT calculation can be improved by accounting for a patient'spre-ejection period (PEP). The PEP can include the difference in timebetween initiation of ventricular contraction (e.g., as detected by anECG) and ejection of blood from the ventricles into the aorta. The PEPcan also be considered as an interval between the onset of the QRScomplex (of an electrocardiogram) and cardiac ejection. PWTT compensatedfor PEP can more accurately represent the propagation time of thearterial pulse from the heart to an extremity. In order to determine thePEP, in one embodiment an acoustic sensor is coupled with the patient todetect a patient's heart sound. The time difference between a feature ofthe ECG signal and a feature of the heart sound (represented as asignal) can be an estimate of PEP. In another embodiment, a bioimpedancesensor can be used to estimate PEP by taking a time difference betweenfeatures of ECG and bioimpedance sensor signals. The arterial PWTT canthen be calculated by subtracting the PEP from the initial PWTTcalculation obtained from the ECG and plethysmograph signals. Thepatient monitor 205 can employ any of the systems or methods fordetermining PWTT and PEP described in more detail in U.S. ProvisionalApplication No. 61/366,862, titled “System for Triggering A Non-InvasiveBlood Pressure Device,” filed Jul. 22, 2010, the disclosure of which ishereby incorporated by reference in its entirety.

In yet other embodiments, the PWTT is determined from a landmark of afirst plethysmograph signal to a landmark of a second plethysmographsignal. In some embodiments, the first plethysmograph signal is acquiredfrom a sensor applied to a finger of a patient and the secondplethysmograph signal is acquired from a sensor applied to a toe of apatient; however other sensor locations can be used as desired and/orrequired.

The analysis of the heart rate and/or PWTT variability can includecorrelation in the time, frequency, or other transform domains. In oneembodiment of a frequency domain analysis, FIG. 3C illustrates powerspectrums 350A, 350B of the PWTT variability and the heart ratevariability of a patient being monitored with the patient monitor 205.The power spectrums 350A, 350B plot power amplitude (having an expandedscale) versus frequency. In one embodiment, the respiratory ratemeasurement is determined from the power spectrums 350A, 350B by thehighest spectral peak in the frequency range corresponding to the normalrange of respiratory rates. The respiratory peak 355 of the powerspectrums 350A, 350B is approximately 0.3 Hz, which corresponds to arespiratory rate of approximately 18 breaths per minute. This is anexample frequency value that can vary for different patients or even forthe same patient over time.

The respiratory rate measurement derived from the PWTT variability andthe respiratory rate measurement from the heart rate variability can becompared with each other and/or with other respiratory rate measurementsto determine an overall respiratory rate measurement or to assessconfidence in a respiratory rate measurement derived from anotherphysiological signal, as described in further detail below.

In certain embodiments, the PWTT and/or heart rate variability data canbe smoothed or otherwise filtered by various signal processing methods,such as moving average smoothing, sliding average smoothing, boxsmoothing, binomial (Gaussian) smoothing, polynomial smoothing, and/orthe like, to improve the accuracy of, or confidence in, the respiratoryrate measurements.

FIG. 4 illustrates an embodiment of a multiparameter patient monitoringsystem 400 coupled to a patient 401. The multiparameter patientmonitoring system 400 includes a patient monitor 405, an acoustic sensor410, and an optical sensor 415. The acoustic sensor 410 and the opticalsensor 415 can obtain physiological signals from the patient 401 andtransmit the signals to the patient monitor 405 through cables 403A,403B.

As shown, the acoustic sensor 410 is attached to the skin of the patient401 on the neck near the trachea. The acoustic sensor 410 can includeadhesive elements (e.g., tape, glue, or the like) to secure the acousticsensor 410 to the skin. The acoustic sensor 410 can additionally besecured to the patient using an anchor 408, which can be affixed near asubclavian region of the patient 401 or at other regions. The anchor 408can reduce stress on the connection between the acoustic sensor 410 andthe skin during movement. Other placement locations for the acousticsensor 410 and the patient anchor 408 are also possible, such as otherparts of the neck, the chest, or the like.

The optical sensor 415 can be removably attached to the finger of thepatient 401. In other embodiments, the optical sensor 415 can beattached to a toe, foot, and/or ear of the patient 401. The opticalsensor 415 can include a reusable clip-type sensor, a disposableadhesive-type sensor, a combination sensor having reusable anddisposable components, or the like. Moreover, the optical sensor 415 canalso include mechanical structures, adhesive or other tape structures,Velcro™ wraps or combination structures specialized for the type ofpatient, type of monitoring, type of monitor, or the like.

In certain embodiments, the various sensors and/or monitors cancommunicate with the patient monitor 405 wirelessly. The wirelesscommunication can employ any of a variety of wireless technologies, suchas Wi-Fi (802.11x), Bluetooth, cellular telephony, infrared, RFID,combinations of the same, and the like.

In certain embodiments, the multiparameter patient monitoring system 200can include additional physiological parameter measurement devices. FIG.5 illustrates an example of a multiparameter respiratory monitoringsystem 500 that includes multiple additional measurement devices. Inparticular, a patient monitor 505 receives inputs from an acousticsensor 510, an optical sensor 515, an electrocardiograph (ECG) 520, acapnograph 525, a bioimpedance monitor 530, and possibly otherphysiological monitors or sensors 535.

In certain embodiments, the multiparameter patient monitor 505 derivesrespiratory rate measurements from signals received from each of thedepicted physiological parameter measurement devices and/or sensors. Incertain embodiments, the respiratory rate measurements derived from oneor more of the optical sensor 515, the ECG 520, the capnograph 525,and/or the bioimpedance monitor 530 can be compared with the respiratoryrate measurement from the acoustic sensor 510. The monitor 505 cancompare one or more of these measurements with the acoustically-derivedmeasurement in order to derive a multiparameter confidence valuereflecting a confidence in the acoustic respiratory rate measurement (orconfidence in any other of the respiratory rate measurements).

In other embodiments, one or more of the respiratory rate measurementsfrom the ECG 520, the capnograph 525 and the bioimpedance monitor 530can be combined with the respiratory rate measurements from the acousticsensor 510 and/or the optical sensor 515 to generate a combinedrespiratory rate output. In certain embodiments, the combinedrespiratory rate output can have greater accuracy than the respiratoryrate measurement obtained from any one of the devices shown.

The ECG 520 can monitor electrical signals generated by the cardiacsystem of a patient. The ECG 520 can include one or more sensors adaptedto be attached to the skin of a patient, which can be used to detectelectrical heart activity of the patient. The ECG 520 can determine anyof a variety of electrical physiological parameters based uponelectrical signals received from the one or more sensors, such as heartrate. In certain embodiments, the ECG 520 can generate anelectrocardiogram waveform. The patient monitor 505 can compare one ormore features of the waveform with an acoustically-derived respiratoryrate measurement to determine multiparameter confidence in theacoustically-derived respiratory rate. For instance, the R-R time periodof the ECG waveform, or the like can be correlated with respiratory ratein certain individuals. More generally, an envelope of the ECG waveformcan include peaks that the patient monitor 505 can correlate infrequency with respiratory rate in certain situations.

The capnograph 525 can determine the carbon dioxide content in inspiredand/or expired air from a patient. For example, the capnograph 525 canmonitor the inhaled and/or exhaled concentration or partial pressure ofcarbon dioxide through a breathing mask or nasal cannula. In certainembodiments, the capnograph 525 can generate a capnogram responsive tothe patient's breathing. The capnograph 525 can also identify end tidalcarbon dioxide (EtCO₂) levels and/or other values. From the EtCO₂values, the capnograph 525 can determine a respiratory rate of thepatient. The capnograph 525 can provide this respiratory ratemeasurement to the patient monitor 505, which can compare therespiratory rate with the acoustically-derived respiratory rate todetermine multiparameter confidence.

The bioimpedance monitor 530 can determine electrical impedance orresistance in body tissue of a medical patient. For example, thebioimpedance monitor 530 can include two or more sensors or electrodespositioned on a patient so as to measure the bioelectrical impedance orresistance across the chest region. The measured bioelectrical impedancecan vary as a result of the expansion of the chest due to breathing, andfrom this variance, a respiratory rate measurement can be derived. Incertain embodiments, the bioimpedance monitor 530 is a TransthoracicImpedance Monitor or the like, having two or more electrodes that canoptionally be combined with ECG electrodes. In other embodiments, thebioimpedance monitor 530 is an impedance tomograph, having many moreelectrodes that can also be used to form a spatial image of theimpedance variation.

The respiratory rate measurement can be derived by the bioimpedancemonitor 530, or alternatively, the bioimpedance monitor 530 can provideimpedance values with respect to time to the patient monitor 505, whichcan derive the respiratory rate. The patient monitor 505 can alsocompare the impedance-derived respiratory rate with theacoustically-derived respiratory rate to determine multiparameterconfidence.

Additional sensors and/or monitors of different types can also beincluded. The other patient monitors 135 can include, for example,thermistor-based breathing sensors or pneumatic breathing belt sensors.

In certain embodiments, the electrocardiograph 520, thecapnograph/capnometer 525, and the bioimpedance monitor 530 arestandalone patient monitors that can provide filtered and/or processedsignals to the patient monitor 505. In other embodiments, theelectrocardiograph 520, the capnograph 525, and the bioimpedance monitor530 can be replaced with respective sensors, which each providephysiological data directly to the patient monitor 505. In still otherembodiments, the acoustic sensor 510 and the optical sensor 515 can bereplaced with an acoustic respiratory monitor and a pulse oximeter,respectively. Thus, any combination of sensors and monitors can provideinputs to the patient monitor 505, including any subset of the devicesshown.

The patient monitor 505 can output for display the respiratory ratevalue derived from the acoustic sensor 550. In addition, the patientmonitor 505 can output respiratory rate values derived from any of theother devices shown.

In certain embodiments, the respiratory rate measurements derived fromone or more of the sensors can be used for sequential hypothesistesting.

FIGS. 6A through 6C illustrate embodiments of systems 600A, 600B, and600C for determining multiparameter confidence of respiratory ratemeasurements and for outputting respiratory rate values. The systems600A, 600B, and 600C can be implemented by any of the patient monitorsdescribed herein, such as the patient monitors 205, 405, and 505, or bythe patient monitors described below. Each of the depicted blocks of thesystems 600A, 600B, and 600C can be implemented by hardware and/orsoftware.

Referring to FIG. 6A, the system 600A receives signal inputs reflectiveof physiological parameters from an acoustic sensor and from an opticalsensor, such as any of the sensors described above. The signal inputscan be received by respiratory rate determination blocks 640 a, 640 b,respectively. Each of the respiratory rate determination blocks 640 candetermine a respiratory rate based at least in part on its respectivesignal input. For example, the respiratory rate determination block 640b can determine respiratory rate of a patient from a time domain orfrequency analysis of a photopleth input signal.

In certain embodiments, the respiratory rate determination block 640 bcan be part of any of the patient monitors described above. Thus, forexample, an optical sensor could provide the photopleth signal to therespiratory rate determination block 640 b of a patient monitor, whichderives a respiratory rate. The respiratory rate determination block 640b could instead be part of a pulse oximetry monitor. The pulse oximetrymonitor could determine a respiratory rate measurement based at least inpart on the photopleth signal. The pulse oximetry monitor could providethe calculated respiratory rate to the patient monitor (e.g., 205, 405,505, or the like).

For convenience, the acoustic respiratory rate measurement will bedescribed using the shorthand RR_(AR) and the photopleth respiratoryrate measurement will be described using the shorthand RR_(PO). In thedepicted embodiment, the RR_(AR) and the RR_(PO) measurements areprovided to a respiratory rate analyzer 645. The respiratory rateanalyzer 645 can analyze the RR_(AR) and the RR_(PO) measurements todetermine a multiparameter confidence in the RR_(AR) measurement. Forexample, the respiratory rate analyzer 645 can compare the twomeasurements to determine a difference between the two measurements. Therespiratory rate analyzer can derive a multiparameter confidence ormultiparameter confidence value from this calculated difference. Incertain embodiments, the greater the difference between the RR_(AR) andthe RR_(PO) measurements, the lower is the multiparameter confidencedetermined for the RR_(AR) measurement. Conversely, in certainembodiments, the respiratory rate analyzer 645 can use the differencebetween the two measurements to assign a multiparameter confidence tothe RR_(PO) measurement.

The respiratory rate analyzer 645 can output for display amultiparameter confidence indicator 660 responsive to the calculatedmultiparameter confidence along an output respiratory rate measurement(RR_(OUT), described below). The multiparameter confidence indicator 660can include a visual and/or audible indication in various embodiments.

Moreover, in certain embodiments, the respiratory rate analyzer 645 cangenerate the respiratory rate output RR_(OUT) based on a combination ofthe inputs RR_(AR) and RR_(PO). For example, the respiratory rateanalyzer 645 could average the two respiratory rate inputs. This averagecould be a weighted average or the like (see, e.g., FIG. 6C).

In another embodiment, the respiratory rate analyzer selects one of therespiratory rate inputs (RR_(AR) and RR_(PO)) to output as therespiratory rate output RR_(OUT). The respiratory rate analyzer 645could make this selection based at least partly on single parameterconfidence values generated by each respiratory rate determination block640 a, 640 b. These single parameter confidence values can reflect aquality of the signal received by each block 640 a, 640 b. Singleparameter confidence values can be distinguished from multiparameterconfidence values, in certain embodiments, in that single parameterconfidence values can reflect confidence that a respiratory rate derivedfrom a single parameter is accurate. In contrast, multiparameterconfidence values can reflect respiratory rate accuracy as determined byan analysis of multiple parameters (e.g., photopleth and ECG).

For example, the respiratory rate determination block 640 b coulddetermine single parameter confidence of the photopleth signal usingtechniques such as those described in U.S. Pat. No. 6,996,427, titled“Pulse Oximetry Data Confidence Indicator,” filed Dec. 18, 2003, (the“'427 patent”) the disclosure of which is hereby incorporated byreference in its entirety. Analogous techniques could be used by therespiratory rate determination block 640 a to determine single parameterconfidence in the quality of the acoustic respiratory signal received.

The respiratory rate analyzer 645 could select either the RR_(AR)respiratory rate value or the RR_(PO) respiratory rate value to provideas the respiratory rate output RR_(OUT) based on, for example, whichsignal has a higher calculated signal quality. In another embodiment,the respiratory rate analyzer 645 could weight a combination of the tworespiratory rate values based at least in part on the single parameterconfidence values. In various embodiments, the respiratory rate analyzer645 can also select the respiratory rate value to output based onpatient-specific factors, such as age, gender, comorbidity, and thelike. For instance, for some patients, one respiratory rate measurementderived from a particular parameter might be more reliable than otherrespiratory rate measurements derived from other parameters. Many othervariations are also possible.

Although the respiratory rate analyzer 645 has been described as beingable to average respiratory rate values or select respiratory ratevalues, the distinction between averaging and selecting can blur.Selecting, for instance, can be considered a subset of weighting whererespiratory rate values selected are given a weight of “1” (orsubstantially 1) and respiratory rate values not selected are given aweight of “0” (or substantially 0).

FIG. 6B extends the embodiment shown in FIG. 6A to include additionalparameter inputs. In FIG. 6B, the system 600B receives an acousticrespiratory signal, a photopleth signal, an ECG signal, a capnographsignal, and a bioimpedance signal. Signal inputs from other types ofsensors and/or monitors, or additional sensors of the types listed, canalso be received. The respective signal inputs are received byrespiratory rate determination blocks 640 a, 640 b, 640 c, 640 d, and640 e. As described above, the respiratory rate determination blocks 640a, 640 b can determine a respiratory rate measurement using any of thetechniques described above and optionally a single parameter confidencevalue based at least in part on its respective signal input. Likewise,the respiratory rate determination blocks 640 c, 640 d, and 640 e cancalculate respiratory rate measurements and optionally single parameterconfidence values.

Signal inputs can also be used to determine respiratory ratemeasurements derived from heart rate variability and/or PWTTvariability. As shown in FIG. 6B, the signal inputs (e.g., an acousticrespiratory signal, a photopleth signal, an ECG signal, a bioimpedancesignal and/or other signals) are received by a heart rate determinationblock 670 and a PWTT determination block 675. In other embodiments, moreor fewer signal inputs can be received by the heart rate determinationblock 670 and/or the PWTT determination block 675. The heart ratedetermination block 670 can derive the patient's heart rate from one ormore of the signal inputs. The PWTT determination block 675 candetermine the patient's PWTT from one or more of the signal inputs usingany of the techniques described above with respect to FIG. 3B.

The respiratory rate determination blocks 640 f and 640 g can determinerespiratory rate measurements based at least in part on an analysis ofthe heart rate variability and the PWTT variability, respectively, ofthe patient, using any of the techniques described above. For example,the respiratory rate determination blocks 640 f and 640 g can determinerespiratory rate measurements from a frequency analysis of heart rateand/or PWTT signals over time. The respiratory rate measurementscalculated by the respiratory rate determination blocks 640 f and 640 gcan be provided to the respiratory rate analyzer 650 along with any ofthe respiratory rate measurements calculated by the respiratory ratedetermination blocks 640 a, 640 b, 640 c, 640 d and 640 e. Therespiratory rate determination blocks 640 f and 640 g can also calculatesingle or multiple parameter confidence values.

The respiratory rate determination blocks 640 can be implemented in anyof the patient monitors 205, 405, 505, etc. described herein. Thus, forexample, a patient monitor can receive sensor inputs from one or more ofan acoustic sensor, an optical sensor, an ECG sensor or sensors, acapnometry sensor, and a bioimpedance sensor. Not all of the inputsshown need by received by a patient monitor; rather, a subset can bereceived by any patient monitor. From the inputs, the patient monitorimplementing the respiratory rate determination blocks 640 can calculateindividual respiratory rate measurements corresponding to each input,using any of the techniques described above. The patient monitor canfurther implement the respiratory rate determination blocks 640 bycalculating single parameter confidence in each block in an analogousmanner to that described in the '427 patent incorporated by referenceabove. In another embodiment, the respiratory rate calculation forcertain of the parameters is performed in a separate monitor. Forinstance, a capnograph monitor can determine a respiratory rate of apatient and provide this respiratory rate value to a respiratory rateanalyzer 650 of the patient monitor.

The respiratory rate determination blocks 640 can provide respiratoryrate values and optionally single parameter confidence values to arespiratory rate analyzer 650. The respiratory rate analyzer 650 canoperate in a similar manner to the respiratory rate analyzer 645described above. For instance, the respiratory rate analyzer 650 cananalyze one or more of the respiratory rate measurements to determine amultiparameter confidence in the RR_(ARM) measurement, using any of thetechniques described above.

In one embodiment, the respiratory rate analyzer 650 determinesmultiparameter confidence by comparing the RR_(AR) measurement to one ormore of the other respiratory rate measurements. The multiparameterconfidence calculated by the respiratory rate analyzer 650 can reflectthe differences between the measurements. For example, the respiratoryrate analyzer 650 can average the differences to generate amultiparameter confidence value, use a weighted average of thedifferences to generate a multiparameter confidence value, can selectthe greatest difference as the multiparameter confidence value, can useany of the above to further derive a multiparameter confidence value(e.g., by looking up the difference value in a look-up table to obtain acorresponding multiparameter confidence value, or by multiplying thedifference value by a scalar to obtain a multiparameter confidencevalue), or by a host of other techniques. Moreover, in certainembodiments, the respiratory rate analyzer 650 can analyze any subset ofthe respiratory rate measurements received to determine a multiparameterconfidence in any given one of the respiratory rate measurements.

The respiratory rate analyzer 650 can output for display amultiparameter confidence indicator 660 responsive to the calculatedmultiparameter confidence along an output respiratory rate measurement(RR_(OUT), described below). The multiparameter confidence indicator 660can include a visual and/or audible indication in various embodiments.The multiparameter confidence indicator 660 can be output to a display655 along with a respiratory rate output RR_(OUT).

Moreover, like the respiratory rate analyzer 645 described above, therespiratory rate analyzer 650 can generate the respiratory rate outputRR_(OUT) based on a combination or selection of any of the respiratoryrate inputs received from the various sensors or monitors. For example,the respiratory rate output RR_(OUT) can be the acoustic respiratoryrate (RR_(AR)), or a selected one of the other respiratory ratemeasurements. Or, the respiratory rate analyzer 650 could average,perform a weighted average (e.g., based on respective single parameterconfidences), or otherwise combine the respiratory rate measurements todetermine the respiratory rate output RR_(OUT). In various embodiments,the respiratory rate analyzer 645 can also select and/or combine therespiratory rate values to determine an output based on patient-specificfactors, such as age, gender, comorbidity, and the like. For instance,for some patients, one respiratory rate measurement derived from aparticular parameter might be more reliable than other respiratory ratemeasurements derived from other parameters. Many other variations arealso possible.

In other embodiments, the combiner/selector module 650 can compare thederived respiratory rate measurements to determine, which, if any, ofthe respiratory rate determination blocks 640 provided outliers. Thecombiner/selector module 650 could reject the outliers and combine(e.g., average) the outputs of the remaining respiratory ratedetermination blocks 640.

In yet other embodiments, the combiner/selector module 650 coulddetermine which of the outputs from the respiratory rate determinationblocks 640 are close to each other (e.g., within a tolerance) and outputa combination of those outputs. For example, if three of the fiverespiratory rate determination blocks 640 produce a similar output andtwo are outliers, the combiner/selector module 650 could average thethree similar outputs or select one of the three outputs as the finalRR_(OUT) measurement. Moreover, the combiner/selector module 650 canlearn over time and can select the output derived from one of thesensors or monitors based on past performance. Many other configurationsand extensions of the combiner/selector module 650 are possible.

In certain embodiments, the respiratory rate output measurements and/orthe multiparameter confidence values can be output to an external deviceover a network, instead of, or in addition to, being output to thedisplay 655. For example, the output data can be output to a centralmonitoring station (such as a nurses' monitoring station), a server, alaptop computer, a cell phone, a smart phone, a personal digitalassistant, other patient monitors, or other clinician devices, forexample. In some embodiments, the patient monitor 505 can transmit datato an external device via a wireless network using a variety of wirelesstechnologies, such as Wi-Fi (802.11x), Bluetooth, cellular telephony,infrared, RFID, combinations of the same, and the like.

FIG. 6C illustrates yet another embodiment of a system 600C forcalculating multiparameter confidence in respiratory rate measurements.In the system 600C, acoustic and photopleth signal inputs are provided,as well as optionally any number of other signal inputs (such as any ofthe inputs described above). As above, respiratory rate determinationblocks 640 a, 640 b, and so forth down to 640 n can receive these signalinputs. The respiratory rate determination blocks 640 a, 640 b, . . . ,640 n can calculate respiratory rate values based on the signal inputs,as well as associated internal confidence values. Each of the internalconfidence values can reflect an individual respiratory rate block 640algorithm's confidence in the respiratory rate measurements.

A respiratory rate analyzer 660 receives the respiratory rate andconfidence measurements 642 calculated by the respiratory ratedetermination blocks 640. The respiratory rate analyzer 660 can havesome or all the features of the respiratory rate analyzers describedabove. In addition, the respiratory rate analyzer 660 can use theinternal confidence values calculated by the respiratory rate blocks 640to weight, select, or otherwise determine appropriate overallrespiratory rate and confidence values 652. The respiratory rateanalyzer 660 outputs these values 652 to a display 655 or to some otherdevice.

The respiratory rate analyzer 660 can use any of a variety of techniquesto calculate the overall respiratory rate and confidence 652. Someexample techniques are described herein. To illustrate, in oneembodiment, the respiratory rate analyzer 660 can perform a weightedaverage of the respiratory rate values from each respiratory ratedetermination block 640. The weights can be derived from, or can be,their respective confidence values.

More complex weighting schemes can also be devised. One exampleweighting algorithm can implement an adaptive algorithm for dynamicallyadjusting the weights applied to each respiratory rate value over time.The weights can be adapted based on minimizing some cost function, suchas may be applied by a Kalman filter, for instance. More generally, anyof a variety of adaptive algorithms may be used to adjust the weights.For example, the respiratory rate analyzer 660 can implement one or moreof the following: a least mean squares algorithm (LMS), a least squaresalgorithm, a recursive least squares (RLS) algorithm, wavelet analysis,a joint process estimator, an adaptive joint process estimator, aleast-squares lattice joint process estimator, a least-squares latticepredictor, a correlation canceller, optimized or frequency domainimplementations of any of the above, any other linear predictor,combinations of the same, and the like.

In another embodiment, the respiratory rate analyzer 660 can select thetop N available sources having the highest confidence level, where N isan integer. For instance, the respiratory rate analyzer 660 can choosethe output of N respiratory rate determination blocks 640 havingconfidence values that exceed a threshold. This threshold may bedetermined relative to the confidence values provided (e.g., via a ratioor the like) or can be an absolute threshold. The respiratory rateanalyzer 660 can then perform a weighted average of the remaining valuesor select from these values, for example, based on confidence values.

Internal confidence of each respiratory rate determination block 640 candepend on a variety of factors, such as signal to noise ratio,irregularities in the data, probe-off conditions, and the like. A probeoff condition, for instance, can result in a zero confidence value, agradual taper down to zero confidence over time, or the like. Likewise,the confidence values can be derived from the signal to noise ratio foreach respiratory rate determination block 640.

FIG. 7 illustrates an example noninvasive multiparameter physiologicalmonitor 700 that can implement any of the features described herein. Anembodiment of the monitor 700 includes a display 701 showing data formultiple physiological parameters. For example, the display 701 caninclude a CRT or an LCD display including circuitry similar to thatavailable on physiological monitors commercially available from MasimoCorporation of Irvine, Calif. sold under the name Radical™, anddisclosed in U.S. Pat. Nos. 7,221,971; 7,215,986; 7,215,984 and6,850,787, for example, the disclosures of which are hereby incorporatedby reference in their entirety. However, many other display componentscan be used that are capable of displaying respiratory rate and otherphysiological parameter data along with the ability to display graphicaldata such as plethysmographs, respiratory waveforms, trend graphs ortraces, and the like.

The depicted embodiment of the display 701 includes a measured value ofrespiratory rate 712 (in breaths per minute (bpm)) and a respiratoryrate waveform graph 706. In addition, other measured blood constituentsshown include SpO₂ 702, a pulse rate 704 in beats per minute (BPM), anda perfusion index 708. Many other blood constituents or otherphysiological parameters can be measured and displayed by themultiparameter physiological monitor 700, such as blood pressure, ECGreadings, EtCO₂ values, bioimpedance values, and the like. In someembodiments, multiple respiratory rates, corresponding to the multipleinput sensors and/or monitors, can be displayed.

FIGS. 8A through 8C illustrate example multiparameter physiologicalmonitor displays 801A-801C that output multiparameter confidenceindicators 814. The multiparameter confidence indicators 814 can begenerated using any of the techniques described above.

Referring to FIG. 8A, an example display 801A is shown that includesparameter data for respiratory rate, including a measured respiratoryrate value 812 in breaths per minute (bpm) and a respiratory waveformgraph 806. The display 801A also includes parameter data for SpO₂ 802and pulse rate 804 in beats per minute (BPM). A respiratory ratemultiparameter confidence indicator 814A is also depicted. In thedepicted embodiment, the multiparameter confidence indicator 814Aincludes text that indicates that the current respiratory rate has a lowmultiparameter confidence level. The multiparameter confidence indicator814A can function as a visual multiparameter confidence-based alarm byflashing, changing color, or the like when the multiparameter confidenceis below a threshold level. The multiparameter confidence indicator caninclude symbols other than (or in addition to) text in certainembodiments. An audible multiparameter confidence-based alarm canalternatively, or additionally, be output through a speaker or otheraudio output device. A multiparameter confidence-based alarm can begenerated as described in the '427 patent described above.

In certain embodiments, an alarm can be output when the monitoredrespiratory rate of the patient deviates beyond a patient-specificand/or patient-independent threshold. The utility and effectiveness ofan alarm based on a respiratory rate measurement determined solely froman acoustic signal can be improved by joint processing of ancillarysignals from multiple monitored physiological parameters, such as thosedescribed herein (e.g., electrical signals, photoplethysmographicsignals, bioimpedance signals, and/or the like).

For example, respiratory rate measurements determined from the ancillarysignals can be used to continuously or periodically refine or assessconfidence in the respiratory rate measurements derived from theacoustic signal. If the multiparameter confidence in the acousticrespiratory rate measurement is low, the alarm can be suppressed, atleast pending further consideration; however, if the multiparameterconfidence in the acoustic respiratory rate measurement is sufficientlyhigh, the alarm can be output without further consideration.

In other embodiments, the ancillary signals can be used to estimate theinitial respiratory rate or timing information to assist an acousticsignal processing algorithm in capturing a respiratory component of theacoustic signal. The use of the ancillary signals from multipleparameters to assist in the capturing of the respiratory component ofthe acoustic signal can lead to increased confidence in the accuracy ofthe respiratory rate measurement, thereby increasing the accuracy,reliability, and effectiveness of the alarm based on the respiratoryrate measurement from the acoustic signal.

The display 801B of FIG. 8B includes the same parameter data as thedisplay 801A. However, the display 801B includes a multiparameterconfidence indicator 814B that indicates the current multiparameterconfidence level numerically, rather than textually (displayed as apercentage in the depicted embodiment). A present multiparameterconfidence of 90% is shown by the multiparameter confidence indicator814B.

The display 800C of FIG. 8C includes a respiratory rate trend graph 816,which depicts respiratory rate measurements over a period of time. Thedisplay 801C also depicts a multiparameter confidence indicator 814C inthe form of a bar graph below the trend graph 816. The multiparameterconfidence indicator 814C includes bars that can correspond tooccurrences of breaths of a patient. The bars can have a height thatcorresponds to a degree of multiparameter confidence in the respiratoryrate measurements for any given breath. As the breaths change over time,the multiparameter confidence can also change over time, resulting in achanging multiparameter confidence indicator 814C. The multiparameterconfidence indicator 814C can be generated using analogous techniques tothose described in the '427 patent described above.

In certain embodiments, the bars can all be depicted with the same colorand/or pattern or with varying colors and/or patterns depending on themultiparameter confidence level. For example, bars within a desiredmultiparameter confidence range can be displayed with a first colorand/or pattern, bars within a tolerable multiparameter confidence rangecan be displayed with a second color and/or pattern, and bars within alow multiparameter confidence range can be displayed with a third colorand/or pattern. In certain embodiments, the bars can be replaced withpulses, lines, or other shapes.

FIG. 8D illustrates another example multiparameter physiological monitor800 having a display 801D. As shown, the physiological monitor 800 canbe configured with a vertical display instead of a horizontal display.The display 801D can include similar parameter data as shown in displays801A-801C. The multiparameter physiological monitor 800 includes amultiparameter confidence indicator 814D that is positioned off thedisplay 801D. The multiparameter confidence indicator 814D can includeone or more light emitting diodes (LEDs) positioned adjacent to text,such as “LOW RR CONF” or “LOW SQ” or “LOW SIQT™,” where SQ and SIQ standfor signal quality and signal intelligence quotient, respectively. Themultiparameter confidence indicator 810D can be activated to inform acaregiver that a measured value of the multiparameter confidence of theincoming signal is below a certain threshold, for instance. In certainembodiments, different colored LEDs can be used to represent differentmultiparameter confidence range levels, such as in the manner describedabove.

The example displays 801A-801D in FIGS. 8A-8D are merely illustrativeexamples. Many other variations and combinations of multiparameterconfidence indicators 814 are also possible in other implementationswithout departing from the spirit and/or scope of the disclosure.

Moreover, in certain embodiments, the features described in U.S. Pat.No. 6,129,675, filed Sep. 11, 1998 and issued Oct. 10, 2000 and in U.S.patent application Ser. No. 11/899,512, filed Sep. 6, 2007, titled“Devices and Methods for Measuring Pulsus Paradoxus,” each of which ishereby incorporated by reference in its entirety, can be used incombination with the features described in the embodiments herein.

FIG. 9 illustrates an embodiment of a patient monitoring process 900 inwhich a user (e.g., a clinician) has the ability to specify a delay timefor an alarm to be triggered. In one implementation, the patientmonitoring process 900 is performed by any of the patient monitoringsystems (e.g., systems 10, 200, 400, 500, 600 a, 600 b) and/or thepatient monitors (e.g., monitors 205, 405, 505, 700, 800) describedabove. More generally, the patient monitoring process 900 can beimplemented by a machine having one or more processors. Advantageously,in certain embodiments, the patient monitoring process 900 provides auser-customizable alarm delay that can reduce nuisance alarms.

Currently available patient monitoring devices often generate alarmsprematurely or generate alarms that may not correspond to a clinicallysignificant event. For example, a monitoring device can generate analarm even though the patient's physiological state or condition doesnot warrant attention. Instead of providing useful, actionableinformation, these “nuisance” alarms can result in unnecessary worry orstress of the patient and/or clinician and wasted time on the part ofthe clinician in responding to the nuisance alarms. The patientmonitoring process 900 can advantageously reduce or suppress the numberof nuisance alarms by providing an alarm delay period. The alarm delayperiod can advantageously be adjusted by a user.

The patient monitoring process 900 begins by receiving user input of analarm delay time at block 902. For example, a user such as a cliniciancan select a desired alarm delay by inputting the desired delay timeinto a physiological monitor via a user interface, a numerical keypad,or the like. The alarm delay time can correspond to a particularphysiological parameter to be monitored. The physiological parameter caninclude, for example, blood pressure, respiratory rate, oxygensaturation (SpO₂) level, other blood constitutions and combinations ofconstitutions, and pulse, among others. The input from the clinician canadjust a default alarm delay. For example, the default alarm delay timemight be 15 seconds, and the clinician input can change the alarm delaytime to 30 seconds.

At block 904, the user-specified alarm delay time is stored in a memorydevice. At block 906, the physiological parameter corresponding to theuser-specified alarm delay time is monitored by a patient monitor of apatient monitoring system. At decision block 908, it is determinedwhether a value of the monitored physiological parameter has remainedpast a threshold (e.g., above or below a threshold or thresholds) forthe user-specified alarm delay time. If it is determined that the valueof the monitored physiological parameter has passed a threshold for thetime period of the user-specified alarm delay, an alarm is output atblock 910. If, however, it is determined that the value of the monitoredphysiological parameter has not remained past the threshold for the timeperiod of the user-specified alarm delay, the patient monitoring process900 loops back to block 906 to continue monitoring. In variousimplementations, the threshold can be set or adjusted by a user (e.g., aclinician) depending on patient-specific factors (e.g., age, gender,comorbidity, or the like).

The alarm can be provided as a visual and/or audible alarm. In oneembodiment, the alarm is output by a patient monitor. In anotherembodiment, the patient monitor transmits the alarm to another device,such as a computer at a central nurses' station, a clinician's end userdevice (e.g., a pod, a pager), or the like, which can be located in ahospital or at a remote location. The patient monitor can transmit thealarm over a network, such as a LAN, a WAN, or the Internet.

As one example, a user can set an alarm delay time for a respiratoryrate to be sixty seconds. In certain situations, a respiratory rate thatis outside a threshold range of values for less than sixty seconds canbe considered an apnea event. Accordingly, an alarm generated before therespiratory rate has remained outside the threshold range of values fora time period of more than sixty seconds may not be desirable or provideuseful information for a clinician to act on. In one embodiment, thepatient monitor can monitor the respiratory rate by receiving signalsfrom an acoustic sensor, such as any of the acoustic sensors describedherein. When the patient monitor determines that the respiratory ratehas been outside a threshold range of values for at least sixty seconds,then an alarm can be output by the patient monitor.

In certain embodiments, an indication can be provided to a user (e.g., aclinician) regarding a current status of the alarm delay period. Theindication can be audible and/or visual. In one embodiment, a confidenceindicator can be altered or modified based on the alarm delay period.For example, the confidence indicator can be modified to reflect a“countdown” to the time of triggering of the alarm. If the confidenceindicator is represented by an LED, for example, the LED can blink oncethe alarm delay has been initiated and can blink faster as the triggertime of the alarm grows closer. If the confidence indicator isrepresented by a bar graph, for example, the bars can be modified duringthe period from initiation of the alarm delay until the time oftriggering of the alarm. A separate countdown timer that is not coupledwith the confidence indicator could also be provided, which counts downseconds remaining in the alarm delay period.

FIG. 10 illustrates an embodiment of a multiparameter patient monitoringprocess 1000. In the multiparameter patient monitoring process 1000, analarm delay time for a first physiological parameter can be modifieddynamically based on a measurement of a second physiological parameter.In one implementation, the patient monitoring process 1000 is performedby any of the patient monitoring systems (e.g., systems 10, 200, 400,500, 600 a, 600 b) and/or the patient monitors (e.g., monitors 205, 405,505, 700, 800) described above. More generally, the patient monitoringprocess 1000 can be implemented by a machine having one or moreprocessors.

At block 1006, first and second physiological parameters are monitoredby a multiparameter patient monitor. In one embodiment, respiratory rateand SpO₂ are the two monitored physiological parameters. In otherembodiments, the first and second monitored physiological parameters caninclude, for example, blood pressure, respiratory rate, oxygensaturation (SpO₂) level, other blood constitutions and combinations ofconstitutions, and pulse, among others.

At decision block 1008, it is determined whether the current monitoredvalue of the first physiological parameter has passed a threshold. Ifso, then at decision block 1010, it is determined whether an alarm delaytime corresponding to the first parameter has been reached. The alarmdelay time can be a default alarm delay time or a user-selected delaytime, such as the user-selected delay time described above with respectto FIG. 9. If the first physiological parameter has not passed thethreshold, the multiparameter patient monitoring process 1000 loops backto block 1006 to continue monitoring.

If it is determined at decision block 1010 that the user-specified alarmdelay time has been reached, then an alarm is output at block 1012. If,however, it is determined that the alarm delay time has not beenreached, then the multiparameter patient monitoring process 1000proceeds to decision block 1014. The alarm can have similar features asdescribed above and can be provided by, on, or to any of the devicesdescribed above.

At decision block 1014, it is determined whether a value of the secondmonitored physiological parameter has deviated from a previous value. Ifso, then the alarm delay time is dynamically modified at block 1016, andthe process 1000 loops back to block 1006 to continue monitoring. Ifnot, the process 1000 loops back to decision block 1006 to continuemonitoring without changing the delay time.

In certain embodiments, a deviation from a previous value for the secondmonitored physiological parameter includes a reduction or increase invalue. In other embodiments, a deviation from a previous value includesa deviation beyond a threshold or threshold range of acceptable values.The threshold or threshold range for the second monitored physiologicalparameter can be set or adjusted by a user (e.g., a clinician) dependingon patient-specific factors (e.g., age, gender, comorbidity, or thelike). The threshold range of values can be set to include any range ofvalues.

In one embodiment, the degree of modification of the alarm delay candepend on the degree of deviation of the second monitored physiologicalparameter. In another embodiment, the degree of modification of thealarm delay can also depend on the value of the user-specified ordefault alarm delay time and/or the identity of the first physiologicalparameter being monitored.

The dynamic modification can be performed in a linear, step-wise,logarithmic, proportional, or any other fashion. For example, the changein the alarm delay corresponding to the first monitored physiologicalparameter can be proportional to the change or deviation in the secondmonitored physiological parameter. In another embodiment, a series ofsuccessive threshold ranges of values of the second physiologicalparameter can be provided, wherein each threshold range corresponds to adifferent amount of delay adjustment.

For example and not by way of limitation, the first physiologicalparameter can be respiratory rate and the second physiological parametercan be SpO₂. In one embodiment, a user-specified or default alarm delaytime can be sixty seconds. If the respiratory rate is less than a giventhreshold for less than the alarm delay time, it can be determinedwhether the current SpO₂ level has deviated. Based at least partly onthis deviation, the alarm delay time can be adjusted. For example, ifthe SpO₂ level has dropped, the alarm delay time can be reduced, forexample, to 30 seconds, or to 15 seconds, or to another value. If thesecond monitored physiological parameter deviates too far beyond athreshold range, an alarm corresponding to the second monitoredphysiological parameter can also be triggered.

FIGS. 11 through 17 illustrate additional example embodiments ofphysiological parameter displays 1100-1700. These displays 1100-1700 canbe implemented by any physiological monitor, including any of themonitors described herein. The displays 1100-1700 shown illustrateexample techniques for depicting parameter values and associatedconfidence. The displays 1100-1700 can be used to depict singleparameter (e.g., internal) confidence, multiparameter confidence, orboth. The displays 1100-1700 can be implemented for respiratory rate orfor any other physiological parameter, including, but not limited to,SpO₂, hemoglobin species (including total hemoglobin), pulse rate,glucose, or any of the other parameters described herein.

Referring initially to FIG. 11, the display 1100 includes an exampleparameter value scale 1102 and a plot area 1106. An indicator 1110displayed in the plot area 1106 plots a parameter value 1108 togetherwith associated confidence. In the depicted embodiment, the indicator1110 is a normal or Gaussian density function (e.g., bell curve) thatincludes a peak 1112. The indicator 1110 can represent a current (ormost recent) parameter value at the peak 1112 and the confidenceassociated with that parameter value.

The value of the parameter at the peak 1112 matches the parameter valuescale 1102. Thus, for instance, the normal density function is centeredat 8.0, and a superimposed value 1108 of “8.0” is superimposed on theindicator 1110, indicating a value of 8.0 for the measured parameter. Ifthe parameter is respiratory rate, the 8.0 can correspond to breaths perminute. If the parameter were hemoglobin (SpHb), the value can bereported as a concentration in g/dL (grams per deciliter) or the like.Other parameters, such as glucose or SpO₂, can have different parametervalue scales. The parameter value scale 1102 and/or the superimposedvalue 1108 are optional and may be omitted in certain embodiments.Likewise, vertical grid lines 1104 are shown but can be optional, andhorizontal grid lines can also be provided.

The confidence is represented in certain embodiments by thecharacteristics of the indicator 1110 as a normal density function (or avariation thereof). The normal density function can be plotted using theGaussian function or bell curve:

${f(x)} = {\frac{1}{\sqrt{2{\pi\sigma}^{2}}}e^{\frac{{({x - \mu})}^{2}}{2\sigma^{2}}}}$where parameters μ and σ² are the mean and variance, respectively. Otherrelated formulas can also be used. In one embodiment, the indicator 1110can be plotted by assigning μ to be the parameter value and σ² (or σ,the standard deviation) to be the computed confidence value (internal,multiparameter, or a combination of the same). Then, the location on theparameter scale 1102 of the indicator 1110 can depend on the value ofthe parameter (μ), and the width or dispersion of the indicator 1110 candepend on the confidence (σ² or σ). Thus, with higher confidence, thevariance (σ²) can be lower, and the curve of the indicator 1110 can benarrower. With lower confidence, the variance can be higher, and thecurve of the indicator 1110 can be wider or more dispersed.

The parameter value and/or confidence can have values that are somelinear combination of μ and σ². For instance, the parameter value can berepresented as αμ, where α is a real number. Likewise, the confidencevalue can be represented as βσ², where β is a real number.

Advantageously, in certain embodiments, the indicator 1110 provides anat-a-glance view of a parameter value and associated confidence. Becausethe confidence can be represented as the width of the indicator 1110,the indicator 1110 can rapidly convey qualitative as well asquantitative information about confidence to a clinician. Mostclinicians may be familiar with the normal density function or itsassociated distribution and may therefore readily associate the shape ofthe indicator 1110 with qualitative meaning regarding confidence.

Other features of the display 1100 include a phantom indicator 1120,shown as dashed lines, that represents the previous-calculated parameterand confidence values. The phantom indicator 1120 can be used for theimmediately previous values, or multiple phantom indicators 1120 can beused for multiple sets of previous values. A safety zone bar 1130 isalso displayed. The safety zone bar 1130 includes three areas—a red zone1132, a yellow zone 1134, and a green zone 1136, representing unsafe,marginally safe, and safe parameter values, respectively. If the peak1112 of the indicator 1110 is in the green zone 1132, the value isrepresented as being safe, and so forth. The colors, including anycolors discussed herein, may be outlines instead of solid colors.Further, the colors can be replaced with hatch marks, lines, dots, orany of a variety of other indications to represent different zones ofsafety.

Some example safety zone ranges for respiratory rate for an adult are asfollows. The red, or danger zone 1132 can include about 5 breaths perminute (BPM) or less. A second red zone (see, e.g., FIG. 12) mightinclude about 30 BPM or more. The yellow, or marginally safe zone 1134,can include about 6 BPM to about 10 BPM. A second yellow zone (see,e.g., FIG. 12) can include about 24 BPM to about 30 BPM. The green zone1336 can include about 11 BPM to about 23 BPM. These ranges are merelyexamples, however, and can vary considerably depending on, for instance,patient age, gender, comorbidity, medications, current activities (e.g.,exercising or sitting), combinations of the same, and the like.

Although the normal density function has been used to illustrateconfidence, other indicators in other embodiments can be illustratedusing different probability density functions (such as binomial orPoisson functions). Further, the indicator need not be illustrated usinga probability density function but can instead be illustrated using oneor more boxes, circles, triangles, or other geometric shapes whosewidth, length, height, or other property changes with changingconfidence (see, e.g., FIG. 16). Further, the characteristics of thedensity function can depend on other factors in addition to or insteadof confidence, such as patient comorbidities (other diseases can affectthe confidence of the measurement), drugs taken by the patient (whichcan also affect the confidence), age, gender, combinations of the same,and the like. Further, the parameter value scale 1102 can changedepending on the range of the parameter being considered, and aclinician can optionally zoom in or zoom out to a smaller or largerrange.

Moreover, the safety ranges on the safety zone bar 1130 can depend on orotherwise be adjusted by a clinician based on patient comorbidity (e.g.,hemoglobinopathy or thalacemia can affect the safe zones forhemoglobin), medications, age, gender, current activities or patientcondition (such as donating blood, which can result in a higher startpoint of the green safety zone for hemoglobin), combinations of thesame, and the like. The alternative implementations described withrespect to FIG. 11, as well as any of the other features of the display1100, can be used for any of the displays 1200-1700 described below aswell.

A variant of the display 1100 is shown as the display 1200 in FIG. 12.The display 1200 also shows an indicator 1210, which can represent anormal density function as described above with respect to FIG. 11. Assuch, the indicator 1210 can represent a parameter value at a peak ofthe indicator 1210 and a confidence value associated with the shape ofthe indicator. In this indicator 1210, however, the indicator 1210itself is colored to show vertical safety zones 1240, 1242, and 1244.These safety zones can be similar to the safety zones described abovewith respect to the safety zone bar 1130 of FIG. 11.

The safety zone 1240 can represent a green or safe zone, the safety zone1242 can represent a yellow or marginal zone, and the zone 1244 canrepresent a red or danger zone. Although the indicator 1210 is centeredon these zones, different values of the parameter represented by theindicator 1210 can shift the indicator 1210 closer toward one or more ofthe zones. Thus, the color of the indicator 1210 can change, forexample, be entirely yellow, or entirely red, or entirely green, or somedifferent combination of the same. Further, the display 1200 can bemodified in some embodiments to add a safety zone bar like the safetyzone bar 1130 of FIG. 11. The colors of the safety zone bar canvertically match the colors above the bar shown in the indicator 1210(see, e.g., FIG. 14).

FIG. 13 depicts another embodiment of a display 1300. Similar to thedisplays 1100, 1200, the display 1300 includes an indicator 1310 thatuses normal density function features to represent a parameter value andconfidence. However, the indicator 1310 includes horizontal safety zones1340, 1342, 1344. The horizontal zones can be similar to the safetyzones described above and can include, for example, red, green, andyellow (or other) colors. In another embodiment (not shown), theindicator 1310 can be a single solid color corresponding to the safetyzone of the peak of the indicator 1310. The indicator 1310 can alsoinclude gradual instead of abrupt transitions between colors.

Referring to FIG. 14, a display 1400 includes an indicator 1410 havingthe density function characteristics described above. In addition, theindicator 1410 is colored vertically with safety zones 1442 and 1444,similar to the indicator 1210 above. In addition, a safety zone bar 1430is also shown, which has colors that correspond vertically to the colorsof the indicator 1410. Thus, a zone 1440 can be green, the zones 1442and 1452 (of the bar 1430) can be yellow, and the zones 1444 and 1454(of the bar) can be red, or the like.

FIG. 15 illustrates a display 1500 having an indicator 1510 withoutcolor. Instead, the indicator 1510, which can include the features ofthe indicators described above, includes markings 1520 to reflectpercentages of standard deviations of the normal density function. Thesestandard deviations can correspond to confidence intervals. Thesemarkings 1520 include horizontal arrows, vertical lines, and associatedpercentage numbers to mark a first standard deviation (e.g., 68%confidence that the parameter lies within the interval marked by thearrow), a second standard deviation (e.g., 95% confidence that theparameter lies within the interval marked by the arrow), and the thirdstandard deviation (e.g., 99% confidence interval). Color or safetyzones can be added to the indicator 1510 as in any of the other exampleindicators described herein.

FIG. 16 illustrates yet another display 1600 with an indicator 1600.Unlike the indicators described above, the indicator 1610 is not a bellcurve but instead a geometric arrangement of vertical bars 1612. Thevertical bars 1612 can approximate a bell curve, however. Horizontalbars may also be used similarly. The width of the vertical bars 1612and/or the width of the indicator 1600 as a whole can represent theconfidence of a parameter. Further, the parameter value can berepresented by the center bar 1612 on a parameter value scale (notshown; see FIG. 11). The narrower the indicator 1610, the moreconfidence is represented, and the wider the indicator 1610, the lessconfidence is represented, in one embodiment.

Referring to FIG. 17, another embodiment of a display 1700 is shown. Thedisplay 1700 illustrates additional features that can be combined withany of the embodiments described above. The display 1700 includes twoindicators 1710 a, 1710 b. Each of the indicators 1710 is asymmetricalinstead of bell-curve shaped. On one side of the peak 1712 a, 1712 b foreach indicator 1710 a, 1710 b, a portion 1714 a, 1714 b of the curve iswider or more dispersed than the other side, leading to the asymmetry.

In one embodiment, the indicator 1710 can be asymmetrical if theconfidence measure indicates higher confidence on one side of theparameter value as opposed to the other. Asymmetric confidence can occurfor some parameters due to bias. For instance, with hemoglobin, a biasfor more positive values at lower values of hemoglobin may occur basedon the levels of other blood constituents such as oxygen saturation(SpO₂) or carboxyhemoglobin (SpCO). Similarly, hemoglobin can have abias for more negative values at higher values of hemoglobin based onlevels of other blood constituents. Thus, for lower values ofhemoglobin, the curve may be wider in the positive direction, and viceversa.

Positive asymmetry for lower parameter values is illustrated by theindicator 1710 a, while negative asymmetry for higher parameter valuesis illustrated by the indicator 1710 b. To illustrate both positive andnegative asymmetry, two indicators 1710 a, 1710 b are depicted on thedisplay 1700. However, in one implementation, only one indicator 1710 isdisplayed. Multiple indicators are also possible, such as for multipleparameters on a single display. Moreover, the features described hereinwith respect to FIG. 17 can be extended to arbitrary geometric shapes. Atriangle, for instance, can have one half that is wider than anotherhalf based on positive or negative bias in confidence levels.

Any of the displays 1100-1700 can be used to indicate the occurrenceand/or severity of an alarm. For instance, the indicators describedabove can pulsate or flash when an alarm occurs, optionally inconjunction with an audible alarm. The seriousness of the alarm candepend at least partially on the measured confidence. Higher confidence(e.g., a narrow indicator) can result in a more urgent alarm, whereasless urgent alarms can result for less confident parameter values. Thisurgency can be displayed in a variety of ways, for example, byincreasing the rate that the indicator flashes, increasing the frequencyand/or pitch of an audible alarm, combinations of the same, and thelike.

Conditional language used herein, such as, among others, “can,” “could,”“might,” “may,” “e.g.,” and the like, unless specifically statedotherwise, or otherwise understood within the context as used, isgenerally intended to convey that certain embodiments include, whileother embodiments do not include, certain features, elements and/orstates. Thus, such conditional language is not generally intended toimply that features, elements and/or states are in any way required forone or more embodiments or that one or more embodiments necessarilyinclude logic for deciding, with or without author input or prompting,whether these features, elements and/or states are included or are to beperformed in any particular embodiment.

Depending on the embodiment, certain acts, events, or functions of anyof the methods described herein can be performed in a differentsequence, can be added, merged, or left out all together (e.g., not alldescribed acts or events are necessary for the practice of the method).Moreover, in certain embodiments, acts or events can be performedconcurrently, e.g., through multi-threaded processing, interruptprocessing, or multiple processors or processor cores, rather thansequentially.

The various illustrative logical blocks, modules, circuits, andalgorithm steps described in connection with the embodiments disclosedherein can be implemented as electronic hardware, computer software, orcombinations of both. To clearly illustrate this interchangeability ofhardware and software, various illustrative components, blocks, modules,circuits, and steps have been described above generally in terms oftheir functionality. Whether such functionality is implemented ashardware or software depends upon the particular application and designconstraints imposed on the overall system. The described functionalitycan be implemented in varying ways for each particular application, butsuch implementation decisions should not be interpreted as causing adeparture from the scope of the disclosure.

The various illustrative logical blocks, modules, and circuits describedin connection with the embodiments disclosed herein can be implementedor performed with a general purpose processor, a digital signalprocessor (DSP), an application specific integrated circuit (ASIC), afield programmable gate array (FPGA) or other programmable logic device,discrete gate or transistor logic, discrete hardware components, or anycombination thereof designed to perform the functions described herein.A general purpose processor can be a microprocessor, but in thealternative, the processor can be any conventional processor,controller, microcontroller, or state machine. A processor can also beimplemented as a combination of computing devices, e.g., a combinationof a DSP and a microprocessor, a plurality of microprocessors, one ormore microprocessors in conjunction with a DSP core, or any other suchconfiguration.

The blocks of the methods and algorithms described in connection withthe embodiments disclosed herein can be embodied directly in hardware,in a software module executed by a processor, or in a combination of thetwo. A software module can reside in RAM memory, flash memory, ROMmemory, EPROM memory, EEPROM memory, registers, a hard disk, a removabledisk, a CD-ROM, or any other form of computer-readable storage mediumknown in the art. An exemplary storage medium is coupled to a processorsuch that the processor can read information from, and write informationto, the storage medium. In the alternative, the storage medium can beintegral to the processor. The processor and the storage medium canreside in an ASIC. The ASIC can reside in a user terminal. In thealternative, the processor and the storage medium can reside as discretecomponents in a user terminal.

While the above detailed description has shown, described, and pointedout novel features as applied to various embodiments, it will beunderstood that various omissions, substitutions, and changes in theform and details of the devices or algorithms illustrated can be madewithout departing from the spirit of the disclosure. As will berecognized, certain embodiments of the inventions described herein canbe embodied within a form that does not provide all of the features andbenefits set forth herein, as some features can be used or practicedseparately from others. The scope of certain inventions disclosed hereinis indicated by the appended claims rather than by the foregoingdescription. All changes which come within the meaning and range ofequivalency of the claims are to be embraced within their scope.

What is claimed is:
 1. A method of triggering an alarm based oninformation related to a physiological parameter, the method comprising:by a processor: providing functionality for a clinician to specify analarm delay time; monitoring a first physiological parameter reflectingphysiological information obtained from a physiological sensor coupledto a patient, the first physiological parameter comprising respiratoryrate; determining whether the first physiological parameter hassatisfied an a first alarm threshold for an amount of time correspondingto the alarm delay time; triggering an alarm in response to saiddetermining that a value of the first physiological parameter hassatisfied the first alarm threshold monitoring a second physiologicalparameter, the second physiological parameter comprising oxygensaturation; determining whether the second physiological parameter hasfallen below a second alarm threshold; automatically reducing the alarmdelay time in response to the second physiological parameter havingfallen below the second alarm threshold; and modifying a display of aconfidence indicator based on the amount of time that the firstphysiological parameter has satisfied the first alarm threshold.
 2. Themethod of claim 1, wherein said automatically reducing the alarm delaytime comprises reducing the alarm delay time to a value that correspondsto an amount that the second physiological parameter has fallen belowthe second alarm threshold.
 3. The method of claim 1, wherein saidproviding functionality for a clinician to specify the alarm delay timecomprises providing a user interface comprising an option to adjust thealarm delay time.
 4. A method of triggering an alarm based oninformation related to a physiological parameter, the method comprising:by a processor: monitoring a first physiological parameter reflectingphysiological information obtained from a physiological sensor coupledto a patient, the first physiological parameter comprising respiratoryrate; automatically adjusting an alarm delay threshold for the firstphysiological parameter based on a value of a second physiologicalparameter, the second physiological parameter comprising oxygensaturation, wherein said adjusting comprises reducing the alarm delaythreshold in response to the value of the second physiological parametermeeting a first threshold; determining whether the first physiologicalparameter has satisfied a second threshold for an amount of timecorresponding to the alarm delay threshold; and triggering an alarm inresponse to said determining that a value of the first physiologicalparameter has satisfied the second threshold for the amount of timecorresponding to the alarm delay threshold.
 5. The method of claim 4,wherein an increase in the oxygen saturation causes said adjusting toincrease the alarm delay time.
 6. The method of claim 4, wherein saidautomatically adjusting the alarm delay time comprises reducing thealarm delay time to a value that corresponds to an amount that thesecond physiological parameter has fallen below the first threshold. 7.The method of claim 4, further comprising providing functionality for auser to adjust the alarm delay time.
 8. The method of claim 4, furthercomprising outputting an indicator configured to reflect an amount oftime which the first physiological parameter has satisfied the secondthreshold.
 9. The method of claim 8, wherein the indicator is aconfidence indicator.
 10. The method of claim 8, wherein said outputtingthe indicator comprises modifying the indicator to reflect a countdownto the alarm delay threshold.
 11. The method of claim 8, wherein saidoutputting the indicator comprises blinking the indicator to indicatethat the value of the first physiological parameter has satisfied thesecond threshold prior to reaching the alarm delay time.
 12. The methodof claim 4, further comprising providing functionality for a clinicianto initially specify the alarm delay time.